Source/colab/Untitled2.ipynb

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  Column  Non-Null Count  Dtype\n","---  ------  --------------  -----\n"," 0   A       3 non-null      int64\n","dtypes: int64(1)\n","memory usage: 156.0 bytes\n"]}]},{"cell_type":"code","source":["friend_dict_list = [\n","    {\n","        \"name\" : \"John\",\n","        \"age\" : 25,\n","        \"job\" : \"student\"\n","    },\n","    {\n","        \"name\" : \"Nate\",\n","        \"age\" : 30,\n","        \"job\" : \"teacher\"\n","    },\n","    {\n","        \"name\" : \"Jenny\",\n","        \"age\" : 30,\n","        \"job\" : \"developer\"\n","    },\n","]\n","\n","df = pd.DataFrame(friend_dict_list)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"EmoE5g5EDx9Z","executionInfo":{"status":"ok","timestamp":1779325589379,"user_tz":-540,"elapsed":13,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"730f2773-608d-4632-8645-6509cda8564c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","0   John   25    student\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-848163e5-61bb-43c5-9a7f-d37beda61fe8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>age</th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>John</td>\n","      <td>25</td>\n","      <td>student</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Nate</td>\n","      <td>30</td>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Jenny</td>\n","      <td>30</td>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-848163e5-61bb-43c5-9a7f-d37beda61fe8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-848163e5-61bb-43c5-9a7f-d37beda61fe8 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"student\",\n          \"teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# job\n","\n","df['job']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"id":"QEo30ICuEdbc","executionInfo":{"status":"ok","timestamp":1779325632138,"user_tz":-540,"elapsed":43,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1d1643b0-1fa4-4e72-90d5-868d518f71e2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0      student\n","1      teacher\n","2    developer\n","Name: job, dtype: object"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>student</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> object</label>"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# 코랩에서 개인 구글 드라이브를 /content/gdrive 이름으로 연결\n","\n","from google.colab import drive\n","\n","drive.mount(\"/content/gdrive\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LVbNvrpWFU1U","executionInfo":{"status":"ok","timestamp":1779325876303,"user_tz":-540,"elapsed":48542,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"59546b82-845a-4044-f8f7-ae1aac5e6d74"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["df.to_csv('/content/gdrive/MyDrive/Colab Notebooks/friends.csv', index = False, encoding=\"utf-8-sig\")"],"metadata":{"id":"swYD3GkRFhdA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# csv 파일을 데이터프레임으로 변환\n","\n","friend_df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/friends.csv')\n","friend_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"kYdddA21HDhY","executionInfo":{"status":"ok","timestamp":1779327276975,"user_tz":-540,"elapsed":27,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"de397b7b-a102-455a-fbf3-9fedbcf41095"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","0   John   25    student\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-164fdcf2-d5d9-4723-a1c7-cacb4ac78ea1\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"student\",\n          \"teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["friend_df['age'] >= 30"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"id":"KIQRBr65LPwI","executionInfo":{"status":"ok","timestamp":1779327375677,"user_tz":-540,"elapsed":8,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1898a57b-77cf-4103-d29b-d650bb756d01"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    False\n","1     True\n","2     True\n","Name: age, dtype: bool"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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30]"],"metadata":{"id":"4iLtTY7yGmLC","colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"status":"ok","timestamp":1779327375845,"user_tz":-540,"elapsed":14,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"c26068e5-89d4-48dd-8b4a-01b711b548ee"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-cbe0a4bd-248c-444f-a741-41e8c1df96a9\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>age</th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Nate</td>\n","      <td>30</td>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Jenny</td>\n","      <td>30</td>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbe0a4bd-248c-444f-a741-41e8c1df96a9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 30,\n        \"max\": 30,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"developer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":54}]},{"cell_type":"code","source":["import seaborn as sns"],"metadata":{"id":"3zxpv6OzK5li"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = sns.load_dataset(\"mpg\")\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":230},"id":"M4Zw-viiL049","executionInfo":{"status":"ok","timestamp":1779327589038,"user_tz":-540,"elapsed":35,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"f9688742-b194-45ea-edc3-bfcfd4d99458"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n","0  18.0          8         307.0       130.0    3504          12.0   \n","1  15.0          8         350.0       165.0    3693          11.5   \n","2  18.0          8         318.0       150.0    3436          11.0   \n","3  16.0          8         304.0       150.0    3433          12.0   \n","4  17.0          8         302.0       140.0    3449          10.5   \n","\n","   model_year origin                       name  \n","0          70    usa  chevrolet chevelle malibu  \n","1          70    usa          buick skylark 320  \n","2          70    usa         plymouth satellite  \n","3          70    usa              amc rebel sst  \n","4          70    usa                ford torino  "],"text/html":["\n","  <div id=\"df-920b51c5-d437-41e8-9ea4-3f246cca71c3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mpg</th>\n","      <th>cylinders</th>\n","      <th>displacement</th>\n","      <th>horsepower</th>\n","      <th>weight</th>\n","      <th>acceleration</th>\n","      <th>model_year</th>\n","      <th>origin</th>\n","      <th>name</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>18.0</td>\n","      <td>8</td>\n","      <td>307.0</td>\n","      <td>130.0</td>\n","      <td>3504</td>\n","      <td>12.0</td>\n","      <td>70</td>\n","      <td>usa</td>\n","      <td>chevrolet chevelle malibu</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>15.0</td>\n","      <td>8</td>\n","      <td>350.0</td>\n","      <td>165.0</td>\n","      <td>3693</td>\n","      <td>11.5</td>\n","      <td>70</td>\n","      <td>usa</td>\n","      <td>buick skylark 320</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>18.0</td>\n","      <td>8</td>\n","      <td>318.0</td>\n","      <td>150.0</td>\n","      <td>3436</td>\n","      <td>11.0</td>\n","      <td>70</td>\n","      <td>usa</td>\n","      <td>plymouth satellite</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16.0</td>\n","      <td>8</td>\n","      <td>304.0</td>\n","      <td>150.0</td>\n","      <td>3433</td>\n","      <td>12.0</td>\n","      <td>70</td>\n","      <td>usa</td>\n","      <td>amc rebel sst</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>17.0</td>\n","      <td>8</td>\n","      <td>302.0</td>\n","      <td>140.0</td>\n","      <td>3449</td>\n","      <td>10.5</td>\n","      <td>70</td>\n","      <td>usa</td>\n","      <td>ford torino</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-920b51c5-d437-41e8-9ea4-3f246cca71c3')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n  \"name\": \"df\",\n  \"rows\": 398,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.815984312565782,\n        \"min\": 9.0,\n        \"max\": 46.6,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          17.7,\n          30.5,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          5,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.26983817119581,\n        \"min\": 68.0,\n        \"max\": 455.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          122.0,\n          307.0,\n          360.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"horsepower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.49115993282855,\n        \"min\": 46.0,\n        \"max\": 230.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          92.0,\n          100.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 846,\n        \"min\": 1613,\n        \"max\": 5140,\n        \"num_unique_values\": 351,\n        \"samples\": [\n          3730,\n          1995,\n          2215\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7576889298126757,\n        \"min\": 8.0,\n        \"max\": 24.8,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          14.7,\n          18.0,\n          14.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 82,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          79,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"usa\",\n          \"japan\",\n          \"europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 305,\n        \"samples\": [\n          \"mazda rx-4\",\n          \"ford f108\",\n          \"buick century luxus (sw)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["# sql group by\n","df.columns\n","df.groupby(by=\"origin\")\n","df.groupby(by=\"origin\").size()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"ZaQYEL3KMFn2","executionInfo":{"status":"ok","timestamp":1779327752064,"user_tz":-540,"elapsed":19,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"4f1bb8ac-51d5-4bc1-f00d-15d4b8d4c2b9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","europe     70\n","japan      79\n","usa       249\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>europe</th>\n","      <td>70</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>249</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["df['origin'].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"uhx4o7KpMtbY","executionInfo":{"status":"ok","timestamp":1779327803368,"user_tz":-540,"elapsed":41,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"d52d2950-6718-44ae-d36d-4e0e6896373e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","usa       249\n","japan      79\n","europe     70\n","Name: count, dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>count</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>usa</th>\n","      <td>249</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>europe</th>\n","      <td>70</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["df.groupby(by=\"origin\")['cylinders'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"sXhyzY1tM583","executionInfo":{"status":"ok","timestamp":1779327915265,"user_tz":-540,"elapsed":43,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"2913283e-c7b7-4008-f0ad-48c26cf514cb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","europe    4.157143\n","japan     4.101266\n","usa       6.248996\n","Name: cylinders, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>cylinders</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>europe</th>\n","      <td>4.157143</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.101266</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.248996</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":68}]},{"cell_type":"code","source":["df.groupby(by=[\"model_year\", \"origin\"])['cylinders'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Qw3TNbsxNVRC","executionInfo":{"status":"ok","timestamp":1779327969341,"user_tz":-540,"elapsed":48,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"fc7ecde8-8c9f-433e-82e1-dbd3e09641b8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["model_year  origin\n","70          europe    4.000000\n","            japan     4.000000\n","            usa       7.636364\n","71          europe    4.000000\n","            japan     4.000000\n","            usa       6.200000\n","72          europe    4.000000\n","            japan     3.800000\n","            usa       6.888889\n","73          europe    4.000000\n","            japan     4.250000\n","            usa       7.241379\n","74          europe    4.000000\n","            japan     4.000000\n","            usa       6.266667\n","75          europe    4.000000\n","            japan     4.000000\n","            usa       6.400000\n","76          europe    4.250000\n","            japan     4.500000\n","            usa       6.363636\n","77          europe    4.000000\n","            japan     4.166667\n","            usa       6.222222\n","78          europe    4.833333\n","            japan     4.000000\n","            usa       6.000000\n","79          europe    4.250000\n","            japan     4.000000\n","            usa       6.260870\n","80          europe    4.111111\n","            japan     4.076923\n","            usa       4.285714\n","81          europe    4.500000\n","            japan     4.333333\n","            usa       4.923077\n","82          europe    4.000000\n","            japan     4.000000\n","            usa       4.300000\n","Name: cylinders, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th></th>\n","      <th>cylinders</th>\n","    </tr>\n","    <tr>\n","      <th>model_year</th>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">70</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>7.636364</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">71</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.200000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">72</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>3.800000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.888889</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">73</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>7.241379</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">74</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.266667</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">75</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.400000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">76</th>\n","      <th>europe</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.500000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.363636</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">77</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.166667</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.222222</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">78</th>\n","      <th>europe</th>\n","      <td>4.833333</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.000000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">79</th>\n","      <th>europe</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.260870</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">80</th>\n","      <th>europe</th>\n","      <td>4.111111</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.076923</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.285714</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">81</th>\n","      <th>europe</th>\n","      <td>4.500000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.333333</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.923077</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">82</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.300000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["df = pd.DataFrame({\"yyyy-mm-dd\" : [\"2005-09-28\", \"2007-10-05\", \"2013-12-20\"]})\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"q5fnn_YIOQDP","executionInfo":{"status":"ok","timestamp":1779328236481,"user_tz":-540,"elapsed":46,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bafd8d11-5b24-4a39-f121-1d2ab1368aa2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   yyyy-mm-dd\n","0  2005-09-28\n","1  2007-10-05\n","2  2013-12-20"],"text/html":["\n","  <div id=\"df-ef4bc30b-38ad-446f-b9f3-8147ee57c34d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>yyyy-mm-dd</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2005-09-28</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2007-10-05</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2013-12-20</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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\"2005-12-20\"\n","\n","data[0:4]\n","\n","data.split(\"-\")\n","data.split(\"-\")[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"39M80FJ2PXhI","executionInfo":{"status":"ok","timestamp":1779328505089,"user_tz":-540,"elapsed":33,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"113c9d01-75e7-40d6-845e-ae267c222b37"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2005'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":89}]},{"cell_type":"code","source":["#  년도 컬럼 추가\n","\n","# 새로운 컬럽 추가\n","# df['year'] = [2005, 2007, 2012]\n","\n","def extract_year(column):\n","  return data.split(\"-\")[0]"],"metadata":{"id":"UnFGi827OYDY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# df에 extract_year() 함수 적용\n","# lamda 함수도 가능\n","\n","df['year'] = df['yyyy-mm-dd'].apply(extract_year)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"F2mDvKynP2oA","executionInfo":{"status":"ok","timestamp":1779328889246,"user_tz":-540,"elapsed":47,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bfe6b609-85dd-495b-e8ee-95724fd32f0d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   yyyy-mm-dd  year\n","0  2005-09-28  2005\n","1  2007-10-05  2005\n","2  2013-12-20  2005"],"text/html":["\n","  <div id=\"df-4422e719-e466-465f-bfd4-46e39e379f9e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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]\n}"}},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["!pip install koreanize_matplotlib"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xKctvdbWTXaE","executionInfo":{"status":"ok","timestamp":1779329553031,"user_tz":-540,"elapsed":10407,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"f8711db5-6887-46de-89f6-02c0cd3228f9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting koreanize_matplotlib\n","  Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl.metadata (992 bytes)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from koreanize_matplotlib) (3.10.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.3.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (4.62.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.5.0)\n","Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (26.1)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (11.3.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (3.3.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.9.0.post0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->koreanize_matplotlib) (1.17.0)\n","Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl (7.9 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m67.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: koreanize_matplotlib\n","Successfully installed koreanize_matplotlib-0.1.1\n"]}]},{"cell_type":"code","source":["# matplotlib (시각화 라이브러리 기본)\n","\n","import matplotlib.pyplot as plt\n","import koreanize_matplotlib"],"metadata":{"id":"eUo14MAQRO__"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot([10,20,30,40])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Ki9qYiTwRDDT","executionInfo":{"status":"ok","timestamp":1779329080312,"user_tz":-540,"elapsed":252,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"716f3a0f-14f0-4213-90df-90b8a884b89b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c252c06e7e0>]"]},"metadata":{},"execution_count":102},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.plot([1,2,3,4], [12,43,25,15])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Qc3zzgEPRywY","executionInfo":{"status":"ok","timestamp":1779329226423,"user_tz":-540,"elapsed":294,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3cf090a7-e44c-4c86-ce95-4b483c497e2e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c2528c0d490>]"]},"metadata":{},"execution_count":108},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["data_a = {\"data_x\" : [1,2,3,4,5,], \"data_y\" : [2,3,5,10,8]}\n","df = pd.DataFrame(data_a)\n","df\n","\n","plt.plot(df['data_x'], df['data_y'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"rmeLIvsVSVT7","executionInfo":{"status":"ok","timestamp":1779329336058,"user_tz":-540,"elapsed":414,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3ed97644-e4bf-4553-dd6b-e1e4e2f1c409"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c2528c0f830>]"]},"metadata":{},"execution_count":112},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(df['data_x'], df['data_y'])\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"J-qeo_ozS4xt","executionInfo":{"status":"ok","timestamp":1779329453563,"user_tz":-540,"elapsed":460,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1bb014de-f99b-4dd5-b74b-c20963c5269b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 5 artists>"]},"metadata":{},"execution_count":116},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAFOVJREFUeJzt3X+s1XX9wPHXFeaF7HIVAvQOECqTBDF/xQhzmqRjyOCflo6KqGmr6w9ildwtMKZ4sTVHJQNiS2gT0bWBTSfMsSFzifIjm7SFUJi3FOjnvXCbN8c93z9ad13BX98+5/W5597HY/v8cT7n3Xm/9rGN5z7nnHvqKpVKJQAAkpxR9gAAwMAiPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVIPLHuCturu747XXXouGhoaoq6srexwA4D2oVCpx/PjxaGpqijPOeOd7G30uPl577bUYO3Zs2WMAAP8PbW1tMWbMmHdc0+fio6GhISL+PfywYcNKngYAeC86Ojpi7NixPf+Ov5M+Fx//eatl2LBh4gMAasx7+ciED5wCAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQ6n3Hx86dO2P27NnR1NQUdXV1sWXLll7PVyqVWLp0aZx33nkxdOjQmDFjRhw8eLCoeQGAGve+46OzszMuueSSWLVq1Wmf//73vx8/+tGPYs2aNfH888/HWWedFTfccEO88cYb//OwAEDte98/LDdz5syYOXPmaZ+rVCqxcuXK+O53vxtz5syJiIif/exnMXr06NiyZUvcdNNN/9u0AEDNK/QzH4cPH44jR47EjBkzes41NjbG1KlT47nnnjvt/6arqys6Ojp6HQBA//W+73y8kyNHjkRExOjRo3udHz16dM9zb9Xa2hrLli0rcgyAUo1f/GTZI9SMV1bMKnsESlD6t11aWlqivb2952hrayt7JACgigqNj3PPPTciIo4ePdrr/NGjR3uee6v6+voYNmxYrwMA6L8KjY8JEybEueeeG9u3b+8519HREc8//3xMmzatyK0AgBr1vj/zceLEiTh06FDP48OHD8eLL74Yw4cPj3HjxsXChQvj3nvvjQsuuCAmTJgQS5Ysiaamppg7d26RcwMANep9x8eePXvi2muv7Xm8aNGiiIiYP39+rF+/Pr7zne9EZ2dn3HrrrfGPf/wjrrrqqti6dWsMGTKkuKkBgJpVV6lUKmUP8d86OjqisbEx2tvbff4DqEm+7fLe+bZL//F+/v0u/dsuAMDAIj4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFSFx8fJkydjyZIlMWHChBg6dGh85CMfiXvuuScqlUrRWwEANWhw0S94//33x+rVq2PDhg0xadKk2LNnTyxYsCAaGxvjjjvuKHo7AKDGFB4fv/zlL2POnDkxa9asiIgYP358PPLII/HCCy8UvRUAUIMKf9vlU5/6VGzfvj1efvnliIj49a9/Hc8++2zMnDnztOu7urqio6Oj1wEA9F+F3/lYvHhxdHR0xMSJE2PQoEFx8uTJWL58ecybN++061tbW2PZsmVFjwHAADN+8ZNlj1AzXlkxq9T9C7/z8dhjj8XDDz8cGzdujH379sWGDRviBz/4QWzYsOG061taWqK9vb3naGtrK3okAKAPKfzOx7e//e1YvHhx3HTTTRERcfHFF8cf/vCHaG1tjfnz55+yvr6+Purr64seAwDoowq/8/HPf/4zzjij98sOGjQouru7i94KAKhBhd/5mD17dixfvjzGjRsXkyZNil/96lfxwAMPxFe+8pWitwIAalDh8fHjH/84lixZEt/4xjfi2LFj0dTUFF/72tdi6dKlRW8FANSgwuOjoaEhVq5cGStXriz6pQGAfsBvuwAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqaoSH3/605/iC1/4QowYMSKGDh0aF198cezZs6caWwEANWZw0S/497//PaZPnx7XXnttPPXUUzFy5Mg4ePBgnHPOOUVvBQDUoMLj4/7774+xY8fGQw891HNuwoQJRW8DANSowt92+cUvfhFXXHFFfO5zn4tRo0bFpZdeGuvWrXvb9V1dXdHR0dHrAAD6r8Lj4/e//32sXr06Lrjggti2bVt8/etfjzvuuCM2bNhw2vWtra3R2NjYc4wdO7bokQCAPqTw+Oju7o7LLrss7rvvvrj00kvj1ltvjVtuuSXWrFlz2vUtLS3R3t7ec7S1tRU9EgDQhxQeH+edd15cdNFFvc59/OMfj1dfffW06+vr62PYsGG9DgCg/yo8PqZPnx4HDhzode7ll1+O888/v+itAIAaVHh8fPOb34xdu3bFfffdF4cOHYqNGzfGT37yk2hubi56KwCgBhUeH1deeWVs3rw5HnnkkZg8eXLcc889sXLlypg3b17RWwEANajwv/MREXHjjTfGjTfeWI2XBgBqnN92AQBSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AINXgsgcAqmf84ifLHqFmvLJiVtkjwIDhzgcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpqh4fK1asiLq6uli4cGG1twIAakBV42P37t2xdu3amDJlSjW3AQBqSNXi48SJEzFv3rxYt25dnHPOOdXaBgCoMVWLj+bm5pg1a1bMmDHjHdd1dXVFR0dHrwMA6L8GV+NFN23aFPv27Yvdu3e/69rW1tZYtmxZNcYAAPqgwu98tLW1xZ133hkPP/xwDBky5F3Xt7S0RHt7e8/R1tZW9EgAQB9S+J2PvXv3xrFjx+Kyyy7rOXfy5MnYuXNnPPjgg9HV1RWDBg3qea6+vj7q6+uLHgMA6KMKj4/rrrsuXnrppV7nFixYEBMnToy77rqrV3gAAANP4fHR0NAQkydP7nXurLPOihEjRpxyHgAYePyFUwAgVVW+7fJWO3bsyNgGAKgB7nwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKkGlz0AA8P4xU+WPULNeGXFrLJHAKgqdz4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIVXh8tLa2xpVXXhkNDQ0xatSomDt3bhw4cKDobQCAGlV4fDzzzDPR3Nwcu3btiqeffjrefPPNuP7666Ozs7PorQCAGjS46BfcunVrr8fr16+PUaNGxd69e+Pqq68uejsAoMYUHh9v1d7eHhERw4cPP+3zXV1d0dXV1fO4o6Oj2iMBACWqanx0d3fHwoULY/r06TF58uTTrmltbY1ly5ZVc4xexi9+Mm2vWvfKillljwBAP1TVb7s0NzfH/v37Y9OmTW+7pqWlJdrb23uOtra2ao4EAJSsanc+brvttnjiiSdi586dMWbMmLddV19fH/X19dUaAwDoYwqPj0qlErfffnts3rw5duzYERMmTCh6CwCghhUeH83NzbFx48Z4/PHHo6GhIY4cORIREY2NjTF06NCitwMAakzhn/lYvXp1tLe3xzXXXBPnnXdez/Hoo48WvRUAUIOq8rYLAMDb8dsuAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAECqqsXHqlWrYvz48TFkyJCYOnVqvPDCC9XaCgCoIVWJj0cffTQWLVoUd999d+zbty8uueSSuOGGG+LYsWPV2A4AqCFViY8HHnggbrnllliwYEFcdNFFsWbNmvjABz4QP/3pT6uxHQBQQwYX/YL/+te/Yu/evdHS0tJz7owzzogZM2bEc889d8r6rq6u6Orq6nnc3t4eEREdHR1FjxYREd1d/6zK6/ZHRf43cN3fO9e9HK57OVz3clTj39j/vGalUnnXtYXHx1/+8pc4efJkjB49utf50aNHx29/+9tT1re2tsayZctOOT927NiiR+N9alxZ9gQDk+teDte9HK57Oap53Y8fPx6NjY3vuKbw+Hi/WlpaYtGiRT2Pu7u7429/+1uMGDEi6urqSpwsR0dHR4wdOzba2tpi2LBhZY8zYLju5XDdy+G6l2OgXfdKpRLHjx+Ppqamd11beHx86EMfikGDBsXRo0d7nT969Gice+65p6yvr6+P+vr6XufOPvvsosfq84YNGzYg/s/Z17ju5XDdy+G6l2MgXfd3u+PxH4V/4PTMM8+Myy+/PLZv395zrru7O7Zv3x7Tpk0rejsAoMZU5W2XRYsWxfz58+OKK66IT37yk7Fy5cro7OyMBQsWVGM7AKCGVCU+Pv/5z8ef//znWLp0aRw5ciQ+8YlPxNatW0/5ECr/ftvp7rvvPuWtJ6rLdS+H614O170crvvbq6u8l+/EAAAUxG+7AACpxAcAkEp8AACpxAcAkEp8lGjnzp0xe/bsaGpqirq6utiyZUvZI/V7ra2tceWVV0ZDQ0OMGjUq5s6dGwcOHCh7rH5v9erVMWXKlJ4/tjRt2rR46qmnyh5rwFmxYkXU1dXFwoULyx6lX/ve974XdXV1vY6JEyeWPVafIj5K1NnZGZdcckmsWrWq7FEGjGeeeSaam5tj165d8fTTT8ebb74Z119/fXR2dpY9Wr82ZsyYWLFiRezduzf27NkTn/nMZ2LOnDnxm9/8puzRBozdu3fH2rVrY8qUKWWPMiBMmjQpXn/99Z7j2WefLXukPqX033YZyGbOnBkzZ84se4wBZevWrb0er1+/PkaNGhV79+6Nq6++uqSp+r/Zs2f3erx8+fJYvXp17Nq1KyZNmlTSVAPHiRMnYt68ebFu3bq49957yx5nQBg8ePBpf1KEf3PngwGtvb09IiKGDx9e8iQDx8mTJ2PTpk3R2dnpJxeSNDc3x6xZs2LGjBlljzJgHDx4MJqamuLDH/5wzJs3L1599dWyR+pT3PlgwOru7o6FCxfG9OnTY/LkyWWP0++99NJLMW3atHjjjTfigx/8YGzevDkuuuiissfq9zZt2hT79u2L3bt3lz3KgDF16tRYv359XHjhhfH666/HsmXL4tOf/nTs378/Ghoayh6vTxAfDFjNzc2xf/9+78UmufDCC+PFF1+M9vb2+PnPfx7z58+PZ555RoBUUVtbW9x5553x9NNPx5AhQ8oeZ8D477fTp0yZElOnTo3zzz8/HnvssfjqV79a4mR9h/hgQLrtttviiSeeiJ07d8aYMWPKHmdAOPPMM+OjH/1oRERcfvnlsXv37vjhD38Ya9euLXmy/mvv3r1x7NixuOyyy3rOnTx5Mnbu3BkPPvhgdHV1xaBBg0qccGA4++yz42Mf+1gcOnSo7FH6DPHBgFKpVOL222+PzZs3x44dO2LChAlljzRgdXd3R1dXV9lj9GvXXXddvPTSS73OLViwICZOnBh33XWX8Ehy4sSJ+N3vfhdf/OIXyx6lzxAfJTpx4kSvEj58+HC8+OKLMXz48Bg3blyJk/Vfzc3NsXHjxnj88cejoaEhjhw5EhERjY2NMXTo0JKn679aWlpi5syZMW7cuDh+/Hhs3LgxduzYEdu2bSt7tH6toaHhlM8znXXWWTFixAifc6qib33rWzF79uw4//zz47XXXou77747Bg0aFDfffHPZo/UZ4qNEe/bsiWuvvbbn8aJFiyIiYv78+bF+/fqSpurfVq9eHRER11xzTa/zDz30UHz5y1/OH2iAOHbsWHzpS1+K119/PRobG2PKlCmxbdu2+OxnP1v2aFC4P/7xj3HzzTfHX//61xg5cmRcddVVsWvXrhg5cmTZo/UZdZVKpVL2EADAwOHvfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJDq/wAfLXh/ayhUeAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"yP0EhomPTmTN","executionInfo":{"status":"ok","timestamp":1779329646399,"user_tz":-540,"elapsed":169,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"97ba4010-9eac-4c60-add7-52261e25515e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 3 artists>"]},"metadata":{},"execution_count":124},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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MAACAMQgTAABgDMIEAAAYgzABAADGIEwAAIAxCBMAAGAMuz935na7lZCQIJfL5TN++PBhvf322+rVq5fsdrvat2/vs3z69Onq3bu3P6cCAABqIb+GSWhoqD7//HOfsZKSErVq1Uq33nqrpO/jJSsrS3a7Xw8NAACCQMAv5bz99tvq3r276tevH+hDAQCAWi7gYZKRkaFRo0YF+jAAACAIBPR6yt69e3X69Gl17dq1yvtwuVw+96wUFBT4Y2oAAMBAAT1jsmDBAiUlJZUbv/vuu9WhQwd16dJFL730kjweT6X7SE9PV1RUlPWIi4sL5JQBAEANCtgZkzNnzuidd97Rvn37fMZzc3MVHR0tScrOztawYcNUXFysiRMnVrif1NRUPfHEE9bzgoIC4gQAgCAVsDMmy5cv15133qlrrrnGZ/x8lEhS8+bNNX36dK1cubLS/TgcDkVGRvo8AABAcApYmCxYsOCibnp1u918dBgAAEgKUJjs2LFDp06d0p133ukzXlxcrO+++856np2drXHjxmnEiBGBmAYAAKhlAnKq4rXXXtMjjzwim83mM3769Gn17dtXpaWlstvtCg8P15gxYzR06NBATAMAANQyAQmTl19+ucLxJk2aaOfOnYE4JAAACAL8iB8AADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYfg+TN998U1dffbXi4+OtR5cuXeR2uyVJubm56tOnjzp27KgOHTpowYIF/p4CAACopez+3mFZWZl69+6tN998s8Ll9957rx599FE9+OCDKiwsVI8ePdS0aVP17t3b31MBAAC1zGW9lLNnzx653W49+OCDkqSIiAhNnTpVCxcuvJzTAAAAhrqsYbJx40YlJib6jHXt2lWbNm2S1+u9nFMBAAAG8vulnAs5fvy4mjVr5jMWHh4up9Op//73v7r22mvLbeNyueRyuaznBQUFAZ8nAACoGX4/Y2Kz2bR161bdfvvtatOmje655x59+umnkqS8vDw5nc5y2zidThUXF1e4v/T0dEVFRVmPuLg4f08ZAAAYwu9hMnDgQH399dfatm2b9u3bp+TkZP3617/WoUOH5HA4VFJSUm6bs2fPKjw8vML9paamKj8/33rk5OT4e8oAAMAQfr+UU7duXeu/bTab+vTpo379+umDDz5QbGysjh496rP+2bNnVVRUpEaNGlW4P4fDIYfD4e9pAgAAA12Wm1/dbrfsdrsSEhK0ZcsWn2Vbt27VTTfdpJAQvusNAIArnd9r4NixYyorK7Oer1q1SuvWrdOAAQPUrVs3lZaWatmyZZKkwsJCTZ48WY899pi/pwEAAGohv1/KWbdunV544QXr8ssNN9ygTZs2KSYmRpK0evVqjR49WjNmzJDb7VZSUpLuu+8+f08DAADUQn4Pk5EjR2rkyJGVLm/WrJnWr1/v78MCAIAgwI0dAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAY/g9TNauXavu3bvrxhtvVPv27ZWcnKzi4mJrud1uV3x8vM9j7dq1/p4GAACohez+3mG9evX0xhtvqEmTJiorK9PDDz+stLQ0zZ49W5LkdruVlZUlu93vhwYAALWc3+ugW7du/3/ndrvGjx+vYcOG+fswAAAgCAX8tMWpU6fkdDqrvL3L5ZLL5bKeFxQU+GNaAADAQAG/+XXBggXVOmOSnp6uqKgo6xEXF+fH2QEAAJMENEzWr1+vXbt2adSoUT7jd999tzp06KAuXbropZdeksfjqXQfqampys/Ptx45OTmBnDIAAKhBAbuUk5OTo9GjR2vVqlVyOBzWeG5urqKjoyVJ2dnZGjZsmIqLizVx4sQK9+NwOHy2BwAAwSsgZ0zOnDmj/v37a9q0afrlL3/ps+x8lEhS8+bNNX36dK1cuTIQ0wAAALWM38PE7XZr8ODB6tWrlx566KGLWp+PDgMAACkAYTJmzBiFh4fr97//fbllxcXF+u6776zn2dnZGjdunEaMGOHvaQAAgFrIr6cqTp8+rfnz5+uGG25Qp06drHGbzaZ169aprKxMffv2VWlpqex2u8LDwzVmzBgNHTrUn9MAAAC1lF/DpEGDBvJ6vRdcZ+fOnf48JAAACCL8iB8AADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAY9hregIAALM0f/r9mp4CalD2jD41enzOmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACMQZgAAABjECYAAMAYhAkAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjFFjYfLqq6+qQ4cO6tixo3r16qVjx47V1FQAAIAhaiRM1q9fr4ULF2rbtm3avXu3hg8frt/85jc1MRUAAGCQGgmTjIwMTZ06VVFRUZKkQYMGKTQ0VLt27aqJ6QAAAEPYa+KgH330kZYuXeozlpiYqA0bNig+Pt5n3OVyyeVyWc/z8/MlSQUFBQGZm8dVHJD9onYI1PvqUvAevLLxHkRNC9R78Px+vV7vBde77GFSVFQku92uunXr+ozHxcVp79695dZPT0/XlClTyo3HxcUFbI64ckW9VNMzwJWO9yBqWqDfg4WFhdYVk4pc9jDJy8uT0+ksN+50OlVcXL7SU1NT9cQTT1jPPR6PTp06pYYNG8pmswV0rleagoICxcXFKScnR5GRkTU9HVyBeA+ipvEeDByv16vCwkI1btz4gutd9jBxOBwqKSkpN3727FmFh4dXuL7D4fAZq1+/fqCmB0mRkZH8gUSN4j2ImsZ7MDAudKbkvMt+8+s111yjs2fPqqioyGc8JydHsbGxl3s6AADAIJc9TGw2m7p06aKtW7f6jG/ZskUJCQmXezoAAMAgNfJx4ccff1xpaWnWHborVqzQmTNndMcdd9TEdPB/HA6HJk+eXO7SGXC58B5ETeM9WPNs3p/63E6AzJ07VxkZGQoJCVF0dLQWLlyoFi1a1MRUAACAIWosTAAAAH6MH/EDAADGIEyuEGvWrNHo0aMvat2mTZtK+v43jUaNGlXhOvPmzdN1111nPd555x2/zRXBq3Pnzhf1g53n34Mffvhhhe/BpUuXqkePHmrZsqVatmyp6667Tr169dKKFSv8PmcEl0t9D3700UeV/j1Yke3bt2vAgAFVnh9q6Cvp4X8pKSnatGmT9dztdquwsFD/+te/VLduXZWWlurcuXPW8latWmnnzp3W5/SfeOIJderUSQ899JD1RXelpaUqLS21tsnOztaBAwckSddff73+9Kc/+cxh3bp1kqQ2bdqoWbNmgXmhMJ7X61X9+vWtn4/4oXPnzlnvqSFDhmjnzp3W11OHhITok08+Uf369a334A/XP2/y5Mn65JNP9Mc//lFt27a1xvfv368nn3xS33zzjSZNmhSolweDrVmzRk899ZT13Ov16tixY9q5c6euv/56Sb7vqTlz5mjx4sXyeDzWNgsXLlRCQkKlfw8uWbJEEyZM0M9+9jNr7MSJE3r55Zd13333lVsfl44wCRIvv/yyz/NTp06pVatWFX7LrvR9uKxcuVJXXXWVJOnAgQO68cYbL3iMb775RpmZmT85l9DQUMLkCnaxv7Px3nvvKT8/XyEhl3bidvPmzZo0aZJPlEjfB/GkSZM0duxYwuQK1a9fP/Xr1896vnv3bg0dOrTSD1Zs3rxZL774orp3737Rx/j22281fvx4jRs3zhqbPHmyjhw5UvWJwwdhEqSWLl2qfv36KTQ0tNJ1oqKirN8s+uFH4/Ly8tS6dWudOXPG5w9sz5491bNnTxUUFOiVV17R5s2bdeLECUVHR6tnz54aPXq0FTq4cp0+fVqNGjX6yfW8Xu8lR4kkDRw4UBMmTNCsWbN06623ql69ejpz5ow+/fRTPfPMMxoxYkRVpo0gc/LkSY0aNUqLFy/Wiy++qD//+c+Svg+L87xe7wX/jrxY/DyKfxEmQejAgQOaPn26Pv30U5/xv/3tb/rss890++23S5K6d+9ufb3/+csw0vdf+X/gwAFlZmZq5cqVPvtwu92666671LVrV2VkZCgmJkbHjh3TCy+8oH79+mnDhg2BfXEw3u7duy/6jFlmZqa8Xq9KS0t19uxZ3X///bLbL/zX0uOPP642bdpoyZIlevLJJ1VUVKTIyEh17txZc+bM0W233eaPl4Fa7Ntvv9XgwYN16tQpff311xo/frzGjx8vSWrfvn3Aj//xxx+rdevWateunVatWhXw4wUbwiTIbNu2TUOGDNErr7yili1b+iwbMGCAFi9eLElq0aKFzz0nLpfroqr/4MGDOnjwoL744gtrrEWLFpo3b57Cw8P173//m58WuMJt2rRJX331lfX++sUvfmFdc//hv1YlKSsrS5Jkt9sVERFhjVd21u68Hj16qEePHoF6Cail3G63Xn31Vc2dO1dvvPGGYmNjNWTIEC1fvlx/+MMfyl3+u5Cfeg/+0I+/deOOO+64qMveqBhhEiROnDih9PR0rVmzRkuWLNGvfvWrC67fvXt3de/e3foDFRYWppEjR0q68GnJVq1aKSIiQkuWLNGwYcNks9nk8XiUkZGhZs2aqUmTJv57Uah1SktL9e677+q2225TRkaGHnvsMe3du9da/uN/rT733HMV7qeis3YzZ87U66+/Xm7dM2fOWJckf2jw4MGV7h/Bx+Px6Oabb9att96qv//972rYsKGk7z9Vs2zZMhUWFl7S/s6/B9etW6fly5db482aNdOECRP02muvWWP/+9//lJGR4Z8XAsIkGLjdbiUmJmrQoEHas2ePz788z2vUqJF1V7oknz9UP/bVV19Jkq677jrdddddPsvCwsK0adMmPf3003r++edlt9vldrvVuXNnbdy4kWutV7iFCxeqQ4cOeu2119S1a1cNGDDAb2fQnnrqKZ9PXJzndDqVk5Pjl2Og9goJCdHGjRvVoEEDn3GbzaahQ4daz2+55RbVq1fPWvbDsx0ul0v//Oc/L/gPrOHDh2v48OF+nj1+iDAJAqGhodq/f79sNpuOHz+uZ555Rh999JE8Ho+8Xq9iYmKUnJysZ555pty2PXr0qPQv9dDQ0Ar/R3D99ddb103r1atX7peicWU6fPiwZs2apQ0bNqhRo0aaOXOmBg0apA8//ND6H8EPhYSEqKioSF6vV8XFxTpy5Ii++eYb9ezZ8yeP5XK5tHXrVh04cEBut1vz5s1TmzZt1LVrV37j5Ap2Pkq8Xq8yMjK0ePFiFRQUyOPxqE6dOrrnnns0Y8YMXXPNNZK+v8w4ZMgQRUREyG63y2azqVWrVpo2bVpNvowrHmESJGw2m4qLi5WYmKjf/e53mjVrlvVR4X/84x9KTk5Wbm6uxo4d67PdhW5WXb58udavX69hw4YFcuoIAnl5eerbt6+ef/55/fznP5ck9e/fX4cOHVJKSoreeOONctsMGjRInTt3VlhYmK6++mo1btxYrVu3VllZ2QWPtXXrVj3wwAOKj49XfHy8pk2bptzcXK1du1YPP/yw/vKXv6hbt24BeZ2oHVJTU7V//36tWLHC+qK0vLw8zZs3T4mJidq9e7fsdruee+65Kl/uu/baa3XixIly4507dyZsqokwCSL79u2T0+lUSkqKz3i7du2Unp6uiRMnlguTC7Hb7dZpzsqu78fGxqp169blxrm+f2Vxu91KS0vT4MGDfcbHjx9f6bX9C11OvJDU1FTNnTtX9957b7llq1atUmpqqrZv316lfSM4ZGZmasmSJVaUSN/fMzJp0iQtW7ZMhw8ftgK6qs6cOVPheGRkpOLj46u17ysdYRJE2rZtK5fLpcWLF2vIkCEKCwuTJB06dEhTpkzRwIEDq7zvyq7vA5LUsGHDclFyXkX3PP2UC30fTqdOnbRmzRrdfPPNiouLs8aPHDmi1atXq0OHDpd8PASXvn37aubMmZo7d66io6MlSYWFhVq4cKHCwsLKfWKxInwnU80hTILIVVddpc2bN+v555/X7Nmz5fF4ZLPZ1KhRIyUnJ+v++++/pP1FR0dXeDYEqKo6der85PeUSNLRo0et9evUqeOzbM6cOZo7d67uu+8+6zdPbDabmjZtqgceeEDJycn+nzhqlfT0dM2fP1/9+/dXUVGRdY9J7969tXnz5mq/ByWpefPmateuXaU3/M+fP1+JiYnVeyFXKJv3xx/ABgAAqCH8ujAAADAGYQIAAIxBmAAAAGMQJgAAwBiECQAAMAZhAgAAjEGYAAAAYxAmAADAGIQJAAAwBmECAACM8f8A6qH+rWMSptwAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.barh(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"0JyNk-W-T74G","executionInfo":{"status":"ok","timestamp":1779329679868,"user_tz":-540,"elapsed":140,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"022cbf17-75ba-4025-aaa7-e7352a83044e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 3 artists>"]},"metadata":{},"execution_count":125},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["values = [175, 187, 184]\n","labels = [\"python\", \"java\", \"c#\"]\n","\n","plt.pie(values, labels=labels, autopct=\"%.1f%%\", startangle=90, counterclock=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":571},"id":"wDYAHv1uUE9H","executionInfo":{"status":"ok","timestamp":1779330401490,"user_tz":-540,"elapsed":553,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"c91f8a37-01ad-4d70-9a6c-b6d63f411558"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.patches.Wedge at 0x7c252b6fe660>,\n","  <matplotlib.patches.Wedge at 0x7c252b6fd370>,\n","  <matplotlib.patches.Wedge at 0x7c25289cc0b0>],\n"," [Text(0.9297090875686453, 0.5879124190662052, 'python'),\n","  Text(0.05693733236902952, -1.0985254390239212, 'java'),\n","  Text(-0.9588938707429671, 0.5390014328845241, 'c#')],\n"," [Text(0.5071140477647156, 0.32067950130883915, '32.1%'),\n","  Text(0.031056726746743372, -0.5991956940130478, '34.2%'),\n","  Text(-0.5230330204052547, 0.2940007815733767, '33.7%')])"]},"metadata":{},"execution_count":161},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]}]}